import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


A						=			logging.get_logger(__name__)





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   'linear'
		lowerCAmelCase_     =   'cosine'
		lowerCAmelCase_     =   'cosine_with_restarts'
		lowerCAmelCase_     =   'polynomial'
		lowerCAmelCase_     =   'constant'
		lowerCAmelCase_     =   'constant_with_warmup'
		lowerCAmelCase_     =   'piecewise_constant'



def 	lowerCamelCase					(	UpperCamelCase     :				Optimizer			,       UpperCamelCase     :				int = -1							)   ->    Optional[Any]:
		return LambdaLR(UpperCamelCase			,       lambda UpperCamelCase							: 1			,       last_epoch=UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				Optimizer			,       UpperCamelCase     :				int			,       UpperCamelCase     :				int = -1							)   ->    Optional[Any]:

		def lr_lambda(UpperCamelCase     :				int							):
				if current_step < num_warmup_steps:
						return float(UpperCamelCase							) / float(max(1.0			,       UpperCamelCase							)							)
				return 1.0

		return LambdaLR(UpperCamelCase			,       UpperCamelCase			,       last_epoch=UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				Optimizer			,       UpperCamelCase     :				str			,       UpperCamelCase     :				int = -1							)   ->    str:
		_lowerCamelCase				   =  {}
		_lowerCamelCase				   =  step_rules.split(','							)
		for rule_str in rule_list[:-1]:
				_lowerCamelCase   ,							_lowerCamelCase				   =  rule_str.split(':'							)
				_lowerCamelCase				   =  int(UpperCamelCase							)
				_lowerCamelCase				   =  float(UpperCamelCase							)
				_lowerCamelCase				   =  value
		_lowerCamelCase				   =  float(rule_list[-1]							)

		def create_rules_function(UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				Optional[int]							):
				def rule_func(UpperCamelCase     :				int							) -> float:
						_lowerCamelCase				   =  sorted(rules_dict.keys()							)
						for i, sorted_step in enumerate(UpperCamelCase							):
								if steps < sorted_step:
										return rules_dict[sorted_steps[i]]
						return last_lr_multiple

				return rule_func

		_lowerCamelCase				   =  create_rules_function(UpperCamelCase			,       UpperCamelCase							)

		return LambdaLR(UpperCamelCase			,       UpperCamelCase			,       last_epoch=UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]			,       UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				Union[str, Any]			,       UpperCamelCase     :				List[str]=-1							)   ->    List[str]:

		def lr_lambda(UpperCamelCase     :				int							):
				if current_step < num_warmup_steps:
						return float(UpperCamelCase							) / float(max(1			,       UpperCamelCase							)							)
				return max(
				    0.0			,       float(num_training_steps - current_step							) / float(max(1			,       num_training_steps - num_warmup_steps							)							)							)

		return LambdaLR(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				Optimizer			,       UpperCamelCase     :				int			,       UpperCamelCase     :				int			,       UpperCamelCase     :				float = 0.5			,       UpperCamelCase     :				int = -1							)   ->    Optional[int]:

		def lr_lambda(UpperCamelCase     :				Union[str, Any]							):
				if current_step < num_warmup_steps:
						return float(UpperCamelCase							) / float(max(1			,       UpperCamelCase							)							)
				_lowerCamelCase				   =  float(current_step - num_warmup_steps							) / float(max(1			,       num_training_steps - num_warmup_steps							)							)
				return max(0.0			,       0.5 * (1.0 + math.cos(math.pi * float(UpperCamelCase							) * 2.0 * progress							))							)

		return LambdaLR(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				Optimizer			,       UpperCamelCase     :				int			,       UpperCamelCase     :				int			,       UpperCamelCase     :				int = 1			,       UpperCamelCase     :				int = -1							)   ->    Optional[Any]:

		def lr_lambda(UpperCamelCase     :				Any							):
				if current_step < num_warmup_steps:
						return float(UpperCamelCase							) / float(max(1			,       UpperCamelCase							)							)
				_lowerCamelCase				   =  float(current_step - num_warmup_steps							) / float(max(1			,       num_training_steps - num_warmup_steps							)							)
				if progress >= 1.0:
						return 0.0
				return max(0.0			,       0.5 * (1.0 + math.cos(math.pi * ((float(UpperCamelCase							) * progress) % 1.0)							))							)

		return LambdaLR(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				str			,       UpperCamelCase     :				Optional[int]			,       UpperCamelCase     :				int			,       UpperCamelCase     :				Optional[int]=1e-7			,       UpperCamelCase     :				Optional[int]=1.0			,       UpperCamelCase     :				int=-1							)   ->    List[Any]:
		_lowerCamelCase				   =  optimizer.defaults['lr']
		if not (lr_init > lr_end):
				raise ValueError(F"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""							)

		def lr_lambda(UpperCamelCase     :				int							):
				if current_step < num_warmup_steps:
						return float(UpperCamelCase							) / float(max(1			,       UpperCamelCase							)							)
				elif current_step > num_training_steps:
						return lr_end / lr_init  # as LambdaLR multiplies by lr_init
				else:
						_lowerCamelCase				   =  lr_init - lr_end
						_lowerCamelCase				   =  num_training_steps - num_warmup_steps
						_lowerCamelCase				   =  1 - (current_step - num_warmup_steps) / decay_steps
						_lowerCamelCase				   =  lr_range * pct_remaining**power + lr_end
						return decay / lr_init  # as LambdaLR multiplies by lr_init

		return LambdaLR(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)


A						=			{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}


def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, SchedulerType]			,       UpperCamelCase     :				Optimizer			,       UpperCamelCase     :				Optional[str] = None			,       UpperCamelCase     :				Optional[int] = None			,       UpperCamelCase     :				Optional[int] = None			,       UpperCamelCase     :				int = 1			,       UpperCamelCase     :				float = 1.0			,       UpperCamelCase     :				int = -1			,       )   ->    Any:
		_lowerCamelCase				   =  SchedulerType(UpperCamelCase							)
		_lowerCamelCase				   =  TYPE_TO_SCHEDULER_FUNCTION[name]
		if name == SchedulerType.CONSTANT:
				return schedule_func(UpperCamelCase			,       last_epoch=UpperCamelCase							)

		if name == SchedulerType.PIECEWISE_CONSTANT:
				return schedule_func(UpperCamelCase			,       step_rules=UpperCamelCase			,       last_epoch=UpperCamelCase							)

		# All other schedulers require `num_warmup_steps`
		if num_warmup_steps is None:
				raise ValueError(F"""{name} requires `num_warmup_steps`, please provide that argument."""							)

		if name == SchedulerType.CONSTANT_WITH_WARMUP:
				return schedule_func(UpperCamelCase			,       num_warmup_steps=UpperCamelCase			,       last_epoch=UpperCamelCase							)

		# All other schedulers require `num_training_steps`
		if num_training_steps is None:
				raise ValueError(F"""{name} requires `num_training_steps`, please provide that argument."""							)

		if name == SchedulerType.COSINE_WITH_RESTARTS:
				return schedule_func(
				    UpperCamelCase			,       num_warmup_steps=UpperCamelCase			,       num_training_steps=UpperCamelCase			,       num_cycles=UpperCamelCase			,       last_epoch=UpperCamelCase			,       )

		if name == SchedulerType.POLYNOMIAL:
				return schedule_func(
				    UpperCamelCase			,       num_warmup_steps=UpperCamelCase			,       num_training_steps=UpperCamelCase			,       power=UpperCamelCase			,       last_epoch=UpperCamelCase			,       )

		return schedule_func(
		    UpperCamelCase			,       num_warmup_steps=UpperCamelCase			,       num_training_steps=UpperCamelCase			,       last_epoch=UpperCamelCase							)
from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class     lowerCAmelCase__     :


		'''simple docstring'''






		lowerCAmelCase_     =   42
		lowerCAmelCase_     =   42





class     lowerCAmelCase__     :


		'''simple docstring'''






		def __init__(       self     :     int     ,  snake_case__     :     int		)      ->	Any:
				_lowerCamelCase				   =  [[] for _ in range(snake_case__		)]
				_lowerCamelCase				   =  size
		def __getitem__(       self     :     Dict     ,  snake_case__     :     int		)      ->	Iterator[Edge]:
				return iter(self._graph[vertex]		)
		@property
		def   _snake_case       (       self     :     str		)      ->	int:
				return self._size
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	Tuple:
				if weight not in (0, 1):
						raise ValueError('Edge weight must be either 0 or 1.'		)

				if to_vertex < 0 or to_vertex >= self.size:
						raise ValueError('Vertex indexes must be in [0; size).'		)

				self._graph[from_vertex].append(Edge(snake_case__     ,  snake_case__		)		)



		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	int | None:
				_lowerCamelCase				   =  deque([start_vertex]		)
				_lowerCamelCase				   =  [None] * self.size
				_lowerCamelCase				   =  0

				while queue:
						_lowerCamelCase				   =  queue.popleft()
						_lowerCamelCase				   =  distances[current_vertex]
						if current_distance is None:
								continue

						for edge in self[current_vertex]:
								_lowerCamelCase				   =  current_distance + edge.weight
								_lowerCamelCase				   =  distances[edge.destination_vertex]
								if (
								    isinstance(snake_case__     ,  snake_case__		)
								    and new_distance >= dest_vertex_distance
								):
										continue
								_lowerCamelCase				   =  new_distance
								if edge.weight == 0:
										queue.appendleft(edge.destination_vertex		)
								else:
										queue.append(edge.destination_vertex		)

				if distances[finish_vertex] is None:
						raise ValueError('No path from start_vertex to finish_vertex.'		)

				return distances[finish_vertex]


if __name__ == "__main__":
				import doctest

				doctest.testmod()